Principal Component Analysis using Singular Value Decomposition for Image Compression

Dash, Prasannajit; Nayak, Maya; Das, Guru Prasad

doi:10.5120/16243-5795

Cited by 7 publications

(1 citation statement)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Overall Centrality Principal Component Analysis (PCA) is a method that can be used to transform high-dimensional data into lower dimensions without reducing or losing a lot of information in the data [35]. In other words, PCA maximizes diversity to retain the information on that data.…”

Section: Network Centralitymentioning

confidence: 99%

Identification of Significant Proteins in Coronavirus Disease 2019 Protein-Protein Interaction Using Principal Component Analysis and ClusterONE

Kusuma¹,

Ramadhani²,

Annisa³

2021

BBRJ

View full text Add to dashboard Cite

Coronavirus Disease 2019 (COVID-19) will cause disease complications and organ damage due to excessive inflammatory reactions if left untreated. Computational analysis of protein-protein interactions can be carried out in various ways, including topological analysis and clustering of protein-protein interaction networks. Topological analysis can identify significant proteins by measuring the most important nodes with centrality measurements. By using Principal Component Analysis (PCA), the types of centrality measures were extracted into the overall centrality value. The study aimed to found significant proteins in COVID-19 protein-protein interactions using PCA and ClusterONE. This study used 57 proteins associated with COVID-19 to obtain protein networks. All of these proteins are homo sapiens organism. The number of proteins and the number of interactions from 57 proteins were 357 proteins and 1686 interactions. The results of this study consisted of two clusters; the best cluster was the first cluster with a lower p-value but had an average overall centrality value that closed to the second clus-ter. There are twenty important proteins in that cluster, and all of these proteins are related to COVID-19. These proteins are expected to be used in the process of discovering medicinal compounds in COVID-19

show abstract

Section: Network Centralitymentioning

confidence: 99%

Identification of Significant Proteins in Coronavirus Disease 2019 Protein-Protein Interaction Using Principal Component Analysis and ClusterONE

Kusuma¹,

Ramadhani²,

Annisa³

2021

BBRJ

View full text Add to dashboard Cite

show abstract

Design of Hotelling T ² control chart using various covariance structures

Nida

Kashif

Khan

et al. 2023

Communications in Statistics - Theory and Methods

View full text Add to dashboard Cite

Comparative Analysis on Dimension Reduction Algorithm of Principal Component Analysis and Singular Value Decomposition for Clustering

Muningsih

Nur

Imaniawan

et al. 2020

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

Clustering is a method of dividing datasets into several groups that have similarity or the same characteristics. High-dimensional Datasets will influence the effectiveness of the grouping process. This study compares two dimension reduction algorithms, namely Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) using K-Means clustering method to find out the best algorithm with the smallest Bouldin Davies Index evaluation. The dataset of this study involved public data from UCIMachine Learning which contains the number of weekly sales of a product. Data processing is performed by comparing the number of clusters from 3 to 10 and the dimension reduction from 2 to 10. From the data processing the RapidMiner tools, application with dimension reduction can provide better results than without dimension reduction. In particular, the PCA algorithm shows better results than the SVD, with which the best number of clusters is 5, and the number of dimensional reductions is 3 with a Bouldin Index of 0.376.

show abstract

Principal Component Analysis using Singular Value Decomposition for Image Compression

Cited by 7 publications

References 9 publications

Identification of Significant Proteins in Coronavirus Disease 2019 Protein-Protein Interaction Using Principal Component Analysis and ClusterONE

Identification of Significant Proteins in Coronavirus Disease 2019 Protein-Protein Interaction Using Principal Component Analysis and ClusterONE

Design of Hotelling T ² control chart using various covariance structures

Comparative Analysis on Dimension Reduction Algorithm of Principal Component Analysis and Singular Value Decomposition for Clustering

Contact Info

Product

Resources

About

Principal Component Analysis using Singular Value Decomposition for Image Compression

Cited by 7 publications

References 9 publications

Identification of Significant Proteins in Coronavirus Disease 2019 Protein-Protein Interaction Using Principal Component Analysis and ClusterONE

Identification of Significant Proteins in Coronavirus Disease 2019 Protein-Protein Interaction Using Principal Component Analysis and ClusterONE

Design of Hotelling T 2 control chart using various covariance structures

Comparative Analysis on Dimension Reduction Algorithm of Principal Component Analysis and Singular Value Decomposition for Clustering

Contact Info

Product

Resources

About

Design of Hotelling T ² control chart using various covariance structures